AI & Automation
AI, automation & intelligent finance
Automation, analytics, and AI applied to finance with CPA-led governance — improving finance operations, decision intelligence, controls, and responsible adoption.
Intelligent finance starts with the finance problem and the desired outcome, not the technology. The work proceeds in a deliberate sequence: control the process, establish trustworthy data, automate what is deterministic, then apply AI where intelligence adds value — with human oversight retained over judgment throughout. The result is finance that operates faster, analyzes more deeply, and decides with evidence, while the record stays trustworthy and the controls hold.
Why this service
Most finance teams do not have an AI problem; they have a process, data, and decision problem that automation and intelligence can address once the foundation is sound. Applying AI to an uncontrolled process only produces faster unreliable output. This engagement brings accounting discipline to the technology — the controls, the reconciliations, the data quality — and then applies automation and intelligence where they measurably improve the operation, the analysis, or the decision. Deterministic automation and AI are kept distinct: automation handles rules-based, repeatable work; AI is applied where pattern recognition, prediction, generation, decision support, or agentic workflows add value, with appropriate human oversight.
Who it suits
Organizations whose finance function is outgrowing manual effort, where the data is reliable enough to support it, and that want intelligence and automation held to a professional standard rather than deployed as an experiment.
- Finance teams spending recurring effort on close, reconciliation, and reporting
- Organizations with reliable data ready to support automation and analytics
- Leadership that wants AI introduced where it is measurable and reviewable
- Finance functions adopting or extending ERP/EPM and wanting the AI capabilities applied
- Teams that need governance over AI-assisted outputs, not just the outputs
What is included
Engagements span operations, decision intelligence, automation, embedded enterprise-finance AI, agentic workflows, and governance — scoped to the problem and the control model.
- Intelligent finance operations: close and reconciliation support, exception and anomaly identification, transaction/document/data review and extraction, workflow routing, recurring reporting support
- FP&A and decision intelligence: forecasting support, variance and driver analysis, scenario analysis, KPI and management-reporting intelligence, cash-flow and performance insight, decision-support workflows
- Finance process automation (deterministic): workflow and approval automation, data validation, repetitive-process automation, exception notifications, reconciliation and reporting workflows, cross-system finance workflows
- AI-enabled ERP, EPM, and enterprise finance: AI capabilities embedded in or connected to finance systems, reporting and analytics, planning and performance use cases, integration, configuration, and implementation support
- AI agents and finance workflows: controlled multi-step workflows, information gathering and analysis, exception evaluation, decision-support assistance, and orchestration — with defined authority boundaries, human review, escalation, and auditability
- AI governance, risk, and controls for finance: data quality and integrity, access and authorization, segregation of duties, human-in-the-loop controls, approval and escalation, output and model validation, audit trails, confidentiality, financial-reporting risk, and governance over AI-assisted outputs
Who brings this
Anpu Rajaratnam's broader professional background includes enterprise finance transformation, ERP/EPM platforms, and AI-related professional certifications and publications. This experience informs the firm's approach to intelligent finance and responsible AI adoption.
What you get
The specific work and expected deliverables are defined before the engagement begins.
- A use-case roadmap that starts from the finance problem, not the technology
- Automation applied to deterministic work before intelligence is layered on top
- Decision-intelligence support that connects analysis to the actual decision
- A control and governance model for any AI-assisted finance output
- Human review, escalation, and auditability built in, not added after
- A clear scope and sequence agreed before the engagement begins
Discuss this for your organization
Request a consultation and the area of interest will be pre-selected. A short conversation confirms scope and fit before any engagement is proposed.